Category Archives: Machine Learning

In this special guest feature, Rachel Roumeliotis, Vice President of Content Strategy at OReilly Media, provides a deep dive into what topics and terms are on the rise in the data science industry, and also touches on important technology trends and shifts in learning these technologies. Rachel leads an editorial team that covers a wide variety of programming topics, ranging from data and AI, to open source in the enterprise, to emerging programming languages. She has been working in technical publishing for 14+ years, acquiring content in many areas, including software development, UX, computer security and AI.

Theres no doubt that artificial intelligence continues to be swiftly adopted by companies worldwide. In just the last few years, most companies that were evaluating or experimenting with AI are now using it in production deployments. When organizations adopt analytic technologies like AI and machine learning (ML), it naturally prompts them to start asking questions that challenge them to think differently about what they know about their business across departments, from manufacturing, production and logistics, to sales, customer service and IT. An organizations use of AI and ML tools and techniques and the various contexts in which it uses them will change as they gain new knowledge.

OReillys learning platform is a treasure trove of information about the trends, topics, and issues tech and business leaders need to know to do their jobs and keep their businesses running. We recently analyzed the platforms user usage to take a closer look at the most popular and most-searched topics in AI and ML. Below are some of the key findings that show where the state of AI and ML is, and where it is headed.

Unrelenting Growth in AI and ML

First and foremost, our analysis found that interest in AI continues to grow. When comparing 2018 to 2019, engagement in AI increased by 58% far outpacing growth in the much larger machine learning topic, which increased only 5% in 2019. When aggregating all AI and ML topics, this accounts for nearly 5% of all usage activity on the platform. While this is just slightly less than high-level, well-established topics like data engineering (8% of usage activity) and data science (5% of usage activity), interest in these topics grew 50% faster than data science. Data engineering actually decreased about 8% over the same time due to declines in engagement with data management topics.

We also discovered early signs that organizations are experimenting with advanced tools and methods. Of our findings, engagement in unsupervised learning content is probably one of the most interesting. In unsupervised learning, an AI algorithm is trained to look for previously undetected patterns in a data set with no pre-existing labels or classification with minimum human supervision or guidance. In 2018, the usage for unsupervised learning topics grew by 53% and by 172% in 2019.

But whats driving this growth? While the names of its methods (clustering and association) and its applications (neural networks) are familiar, unsupervised learning isnt as well understood as its supervised learning counterpart, which serves as the default strategy for ML for most people and most use cases. This surge in unsupervised learning activity is likely driven by a lack of familiarity with its uses, benefits, and requirements by more sophisticated users who are faced with use cases not easily addressed with supervised methods.

Deep Learning Spurs Interest in Other Advanced Techniques

While deep learning cooled slightly in 2019, it still accounted for 22% of all AI and ML usage. We also suspect that its success has helped spur the resurrection of a number of other disused or neglected ideas. The biggest example of this is reinforcement learning. This topic experienced exponential growth, growing over 1,500% since 2017.

Even with engagement rates dropping by 10% in 2019, deep learning itself is one of the most popular ML methods among companies that are evaluating AI, with many companies choosing the technique to support production use cases. It might be that engagement with deep learning topics has plateaued because most people are already actively engaging with the technology, meaning growth could slow down.

Natural language processing is another topic that has showed consistent growth. While its growth rate isnt huge it grew by 15% in 2018 and 9% in 2019 natural language processing accounts for about 12% of all AI and ML usage on our platform. This is around 6x the share of unsupervised learning and 5x the share of reinforcement learning usage, despite the significant growth these two topics have experienced over the last two years.

Not all AI/ML methods are treated equally, however. For example, interest in chatbots seems to be waning, with engagement decreasing by 17% in 2018 and by 34% in 2019. This is likely because chatbots were one of the first application of AI and is probably a reflection of the relative maturity of its application.

The growing engagement in unsupervised learning and reinforcement learning demonstrates that organizations are experimenting with advanced analytics tools and methods. These tools and techniques open up new use cases for businesses to experiment and benefit from, including decision support, interactive games, and real-time retail recommendation engines. We can only imagine that organizations will continue to use AI and ML to solve problems, increase productivity, accelerate processes, and deliver new products and services.

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Key Trends Framing the State of AI and ML - insideBIGDATA

Alejandro Saucedosays hecould spend hours talking about solutions to bias in machine learning algorithms.

In fact, he has already spent countless hours on the topic via talks at events and in his day-to-day work.

Its an area he is uniquely qualified to tackle. He is engineering director of machine learning at London-based Seldon Technologies, and chief scientist at The Institute for Ethical AI and Machine Leaning.

His key thesis is that the bias which creeps into AI a problem farfrom hypotheticalcannotbe solved with more tech but with the reintroduction of human expertise.

In recent years countless stories detail how AI decisioning has resulted in women being less likely to qualify for loans, minorities being unfairly profiled by police, and facial recognition technology performing more accurately when analysing white, male faces.

You are affecting people's lives," hetellsBusinessCloud, in reference tothe magnitudeofthese automated decisionsin the security and defence space, and even in the judicial process.

Saucedoexplains that machine learning processes are, by definition, designed to be discriminatory but not like this.

"The purpose of machine learning is to discriminate toward a right answer, he said.

"Humans are not born racist, and similarly machine learning algorithms are not by default going to be racist. Theyare a reflection ofthedata ingested."

Ifalgorithms adopt human bias from our biased data, removing biastherefore suggeststhetechnology has great potential.

But the discussionoftenstopsat this theoretical level oracts asa cue for engineers to fine-tune the software in the hopes of a more equitable outcome.

Its not that simple, Saucedo suggests.

An ethical question of that magnitude shouldn't fall onto the shoulders of a single data scientist. They will not have the full picture in order to make a call that could have impact on individuals across generations, he says.

Instead the approach with the most promise takes one step further back from the problem.

Going beyond the algorithm, as he puts it, involves bringing in human experts, increasing regulation, and a much lighter touch when introducing the technology at all.

Instead of just dumping an entire encyclopaedia of an industry into a neural network to learn from scratch, you can bring in domain experts to understand how these machines learn, heexplains.

This approach allows those making the technology to better explain why an algorithm makes the choices it does something which is almost impossible with the black box of a neural network working on its own.

For instance, a lawyer could help with the building of a legal AI, to guide and review the machine learning's output for nuances even small things like words which are capitalised.

In this way, he says, the resulting machine learning becomes easier to understand.

This approach means automating a percentage of the process, and requiring a human for the remainder, or what he calls 'human augmentation' or 'human manual remediation'.

This could slow down the development of potentially lucrative technology battling to win the AI arms race but it was a choice he said would ultimately be good for business and people.

"You either take the slow and painful route which works, or you take the quick fix which doesn't, he says.

Saucedo is only calling for red tape which is proportionate to its potential impact. In short, a potential 'legal sentencing prediction system' needs more governance than a prototype being tested on a single user.

He saysanyone building machine learning algorithms with societal impact should be asking how they can build a process which still requires review from human domain expertise.

"If there is no way to introduce a human in to review, the question is: should you even be automating that process? If you should, you need to make sure that you have the ethics structure and some form of ethics board to approve those use cases."

And while his premise is that bias is not a single engineer's problem, he said that this does not make them now exempt.

"It is important as engineers, individuals and as people providing that data to be aware of the implications. Not only because of the bad usecases, butbeing aware that most of the incorrect applications of machine learning algorithms are not done through malice but lack of best practice."

This self-regulation might be tough for fast-paced AI firms hoping to make sales, but conscious awareness on the part of everyone building these systemsis a professional responsibility,he says.

And even self-regulation is only the first step. Good ethics alone does not guarantee a lack of blind spots.

That's why Saucedo also suggests external regulationandthis doesn't have to slow down innovation.

"When you introduce regulations that are embedded with what is needed, things are done the right way. And when they're done the right way, they're more efficient and there is more room for innovation."

For businesses looking to incorporate machine learning, rather than building it, he points to The Institute for Ethical AI & Machine Learnings AI-RFX Procurement Framework.

The idea is to abstract the initial high-level principles created at The Institute, such as the human augmentation mentioned earlier, and trust and privacy by design. It breaks these principles down into a security questionnaire.

"We've taken all of these principles, and we realised that understanding and agreeing on exact best-practice is very hard. What is universally agreed is what bad practice is."

This, along with access to the right stakeholders to evaluate the data and content,is enough to sort mature AI businesses from those "selling snake oil".

The institute is also contributing to some of the official industry standards that are being created for organisations like the police and the ISO, he explains.

And the work is far from done if a basic framework and regulation can be created with enough success to be adopted internationally, even differing Western and Eastern ethics need to be accounted for.

"In the West you have good and bad, and in theEastit is more about balance," he says.

There are also the differing concepts of theself versusthe community. The considerations quickly become philosophical and messy a sign that they are a little bit more human.

"If we want to reach international standards and regulation, we need to be able to align on those foundational components, to know where everyone is coming from, he says.

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Why AI bias can't be solved with more AI - BusinessCloud

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

PLoS Med. 2020 Jun;17(6):e1003149

Authors: Atabaki-Pasdar N, Ohlsson M, Viuela A, Frau F, Pomares-Millan H, Haid M, Jones AG, Thomas EL, Koivula RW, Kurbasic A, Mutie PM, Fitipaldi H, Fernandez J, Dawed AY, Giordano GN, Forgie IM, McDonald TJ, Rutters F, Cederberg H, Chabanova E, Dale M, Masi F, Thomas CE, Allin KH, Hansen TH, Heggie A, Hong MG, Elders PJM, Kennedy G, Kokkola T, Pedersen HK, Mahajan A, McEvoy D, Pattou F, Raverdy V, Hussler RS, Sharma S, Thomsen HS, Vangipurapu J, Vestergaard H, t Hart LM, Adamski J, Musholt PB, Brage S, Brunak S, Dermitzakis E, Frost G, Hansen T, Laakso M, Pedersen O, Ridderstrle M, Ruetten H, Hattersley AT, Walker M, Beulens JWJ, Mari A, Schwenk JM, Gupta R, McCarthy MI, Pearson ER, Bell JD, Pavo I, Franks PW

AbstractBACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or 5%) rather than a continuous one.CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

PMID: 32559194 [PubMed as supplied by publisher]

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. - DocWire...

The global Machine Learning in Finance Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning in Finance Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning in Finance Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning in Finance Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning in Finance Market.

Leading players of the global Machine Learning in Finance Market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning in Finance Market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning in Finance Market. It also provides useful recommendations for new as well as established players of the global Machine Learning in Finance Market.

Final Machine Learning in Finance Report will add the analysis of the impact of COVID-19 on this Market.

Machine Learning in Finance Market competition by top manufacturers/Key player Profiled:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

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With the slowdown in world economic growth, the Machine Learning in Finance industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Machine Learning in Finance market size to maintain the average annual growth rate of 15 from XXX million $ in 2014 to XXX million $ in 2019, This Report analysts believe that in the next few years, Machine Learning in Finance market size will be further expanded, we expect that by 2024, The market size of the Machine Learning in Finance will reach XXX million $.

Segmentation by Product:

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Segmentation by Application:

BanksSecurities Company

Competitive Analysis:

Global Machine Learning in Finance Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Machine Learning in Finance Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Scope of the Report:The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Machine Learning in Finance Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.

Key Questions Answered:

Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions. We acknowledge everyone who is doing their part in this financial and healthcare crisis.

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Table of Contents

Report Overview:It includes major players of the global Machine Learning in Finance Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Machine Learning in Finance Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Machine Learning in Finance Market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the global Machine Learning in Finance Market by application, it gives a study on the consumption in the global Machine Learning in Finance Market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the global Machine Learning in Finance Market are profiled in this section. The analysts have provided information about their recent developments in the global Machine Learning in Finance Market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Machine Learning in Finance Market.

Key Findings: This section gives a quick look at important findings of the research study.

About Us:Report Hive Research delivers strategic market research reports, statistical surveys, industry analysis and forecast data on products and services, markets and companies. Our clientele ranges mix of global business leaders, government organizations, SMEs, individuals and Start-ups, top management consulting firms, universities, etc. Our library of 700,000 + reports targets high growth emerging markets in the USA, Europe Middle East, Africa, Asia Pacific covering industries like IT, Telecom, Semiconductor, Chemical, Healthcare, Pharmaceutical, Energy and Power, Manufacturing, Automotive and Transportation, Food and Beverages, etc. This large collection of insightful reports assists clients to stay ahead of time and competition. We help in business decision-making on aspects such as market entry strategies, market sizing, market share analysis, sales and revenue, technology trends, competitive analysis, product portfolio, and application analysis, etc.

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Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill...

Deep learning applications usually involve complex optimisation problems that are often difficult to solve analytically. Often the objective function itself may not be in analytically closed-form, which means that the objective function only permits function evaluations without any gradient evaluations. This is where Zeroth-Order comes in.

Optimisation corresponding to the above types of problems falls into the category of Zeroth-Order (ZO) optimisation with respect to the black-box models, where explicit expressions of the gradients are hard to estimate or infeasible to obtain.

Researchers from IBM Research and MIT-IBM Watson AI Lab discussed the topic of Zeroth-Order optimisation at the on-going Computer Vision and Pattern Recognition (CVPR) 2020 conference.

In this article, we will take a dive into what Zeroth-Order optimisation is and how this method can be applied in complex deep learning applications.

Zeroth-Order (ZO) optimisation is a subset of gradient-free optimisation that emerges in various signal processing as well as machine learning applications. ZO optimisation methods are basically the gradient-free counterparts of first-order (FO) optimisation techniques. ZO approximates the full gradients or stochastic gradients through function value-based gradient estimates.

Derivative-Free methods for black-box optimisation has been studied by the optimisation community for many years now. However, conventional Derivative-Free optimisation methods have two main shortcomings that include difficulties to scale to large-size problems and lack of convergence rate analysis.

ZO optimisation has the following three main advantages over the Derivative-Free optimisation methods:

ZO optimisation has drawn increasing attention due to its success in solving emerging signal processing and deep learning as well as machine learning problems. This optimisation method serves as a powerful and practical tool for evaluating adversarial robustness of deep learning systems.

According to Pin-Yu Chen, a researcher at IBM Research, Zeroth-order (ZO) optimisation achieves gradient-free optimisation by approximating the full gradient via efficient gradient estimators.

Some recent important applications include generation of prediction-evasive, black-box adversarial attacks on deep neural networks, generation of model-agnostic explanation from machine learning systems, and design of gradient or curvature regularised robust ML systems in a computationally-efficient manner. In addition, the use cases span across automated ML and meta-learning, online network management with limited computation capacity, parameter inference of black-box/complex systems, and bandit optimisation in which a player receives partial feedback in terms of loss function values revealed by her adversary.

Talking about the application of ZO optimisation to the generation of prediction-evasive adversarial examples to fool DL models, the researchers stated that most studies on adversarial vulnerability of deep learning had been restricted to the white-box setting where the adversary has complete access and knowledge of the target system, such as deep neural networks.

In most of the cases, the internal states or configurations and the operating mechanism of deep learning systems are not revealed to the practitioners, for instance, Google Cloud Vision API. This in result gives rise to the issues of black-box adversarial attacks where the only mode of interaction of the adversary with the system is through the submission of inputs and receiving the corresponding predicted outputs.

ZO optimisation serves as a powerful and practical tool for evaluating adversarial robustness of deep learning as well as machine learning systems. ZO-based methods for exploring vulnerabilities of deep learning to black-box adversarial attacks are able to reveal the most susceptible features.

Such methods of ZO optimisation can be as effective as state-of-the-art white-box attacks, despite only having access to the inputs and outputs of the targeted deep neural networks. ZO optimisation can also generate explanations and provide interpretations of prediction results in a gradient-free and model-agnostic manner.

The interest in ZO optimisation has grown rapidly over the last few decades. According to the researchers, ZO optimisation has been increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult to compute or infeasible to obtain.

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Zeroth-Order Optimisation And Its Applications In Deep Learning - Analytics India Magazine

Market Overview

Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry.

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Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly.

The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Market Analysis

According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.

Segmentation by Components

The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.

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Segmentation by End-users

The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.

Segmentation by Deployment Mode

The market has been analyzed and segmented by the following deployment mode, namely public and private.

Regional Analysis

The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

Vendor Analysis

Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.

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Benefits

The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

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Machine Learning As A Service In Manufacturing Market Impact Of Covid-19 And Benchmarking - Cole of Duty